{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "utO0kCtofBQ3",
    "outputId": "ba25bd17-7106-4d42-a44b-f55d3d946014"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: gputil in /usr/local/lib/python3.6/dist-packages (1.4.0)\n",
      "Requirement already satisfied: psutil in /usr/local/lib/python3.6/dist-packages (5.4.8)\n",
      "Requirement already satisfied: humanize in /usr/local/lib/python3.6/dist-packages (0.5.1)\n"
     ]
    }
   ],
   "source": [
    "# import the necessary packages\n",
    "from keras.models import Sequential\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.convolutional import MaxPooling2D\n",
    "from keras.layers.core import Activation\n",
    "from keras.layers.core import Flatten\n",
    "from keras.layers.core import Dense\n",
    "from keras import backend as K\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.datasets import mnist\n",
    "from keras.optimizers import SGD\n",
    "from keras.utils import np_utils\n",
    "from keras import backend as K\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = (15,15)\n",
    "import timeit\n",
    "from google.colab.patches import cv2_imshow\n",
    "!pip install gputil\n",
    "!pip install psutil\n",
    "!pip install humanize\n",
    "import keras\n",
    "import functools\n",
    "import os,sys,humanize,psutil,GPUtil\n",
    "import subprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XLP7jdgVdP-T"
   },
   "outputs": [],
   "source": [
    "## Mish Activation Function\n",
    "def mish(x):\n",
    "\treturn tf.keras.layers.Lambda(lambda x: x*tf.tanh(tf.log(1+tf.exp(x))))(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IYQJhJdYdPu-"
   },
   "outputs": [],
   "source": [
    "## Data Parameters for the network\n",
    "inputShape=(28,28,1)\n",
    "numClasses=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 408
    },
    "colab_type": "code",
    "id": "8ej0svR5dLX2",
    "outputId": "9bac70d5-dc37-42fb-f9a0-fa95ca00759c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 28, 28, 20)        520       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 14, 14, 20)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 14, 14, 50)        25050     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 50)          0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 2450)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 500)               1225500   \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                5010      \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 1,256,080\n",
      "Trainable params: 1,256,080\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "##LeNet Architecture\n",
    "model = Sequential()\n",
    "model.add(Conv2D(20, 5, padding=\"same\",input_shape=inputShape, activation = mish))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(50, 5, padding=\"same\",activation = mish ))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(500, activation = mish))\n",
    "\n",
    "\n",
    "model.add(Dense(numClasses))\n",
    "\n",
    "model.add(Activation(\"softmax\"))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "colab_type": "code",
    "id": "qir7lTzDURT0",
    "outputId": "5ce796a7-42e8-40aa-c82d-09d81fad71f1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tue May 14 13:09:42 2019       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 410.79       Driver Version: 410.79       CUDA Version: 10.0     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   62C    P0    29W /  70W |   1596MiB / 15079MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                       GPU Memory |\n",
      "|  GPU       PID   Type   Process name                             Usage      |\n",
      "|=============================================================================|\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "#GPU-Info\n",
    "!ln -sf /opt/bin/nvidia-smi /usr/bin/nvidia-smi\n",
    "print(subprocess.getoutput('nvidia-smi'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dfx9dWxKVHog"
   },
   "outputs": [],
   "source": [
    "## Defining Top-K Accuracy Metric\n",
    "top3_acc = functools.partial(keras.metrics.top_k_categorical_accuracy, k=3)\n",
    "top3_acc.__name__ = 'top3_acc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "IJkZX0fnfac_",
    "outputId": "6732c263-1a62-42c8-8995-ab3640a86031"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading MNIST Dataset\n"
     ]
    }
   ],
   "source": [
    "##MNIST Dataset Download\n",
    "print(\"Downloading MNIST Dataset\")\n",
    "((trainData, trainLabels), (testData, testLabels)) = mnist.load_data()\n",
    "\n",
    "##Data Reshaping based on channel orientation\n",
    "if K.image_data_format() == \"channels_first\":\n",
    "\ttrainData = trainData.reshape((trainData.shape[0], 1, 28, 28))\n",
    "\ttestData = testData.reshape((testData.shape[0], 1, 28, 28))\n",
    "else:\n",
    "\ttrainData = trainData.reshape((trainData.shape[0], 28, 28, 1))\n",
    "\ttestData = testData.reshape((testData.shape[0], 28, 28, 1))\n",
    "\n",
    "# scale data to the range of [0, 1]\n",
    "trainData = trainData.astype(\"float32\") / 255.0\n",
    "testData = testData.astype(\"float32\") / 255.0\n",
    "\n",
    "## Generating Data Label Vectors\n",
    "trainLabels = np_utils.to_categorical(trainLabels, 10)\n",
    "testLabels = np_utils.to_categorical(testLabels, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 901
    },
    "colab_type": "code",
    "id": "SrjXlu8Jd-F6",
    "outputId": "d10c8650-bd25-4bca-e626-5198b5ab0067"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling LeNet\n",
      "Training LeNet\n",
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 4s 68us/step - loss: 1.4091 - acc: 0.6368 - top_k_categorical_accuracy: 0.9020 - top3_acc: 0.8219\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 4s 62us/step - loss: 0.2924 - acc: 0.9121 - top_k_categorical_accuracy: 0.9962 - top3_acc: 0.9848\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 4s 62us/step - loss: 0.1931 - acc: 0.9427 - top_k_categorical_accuracy: 0.9982 - top3_acc: 0.9920\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 4s 62us/step - loss: 0.1443 - acc: 0.9571 - top_k_categorical_accuracy: 0.9989 - top3_acc: 0.9949\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 4s 62us/step - loss: 0.1162 - acc: 0.9657 - top_k_categorical_accuracy: 0.9993 - top3_acc: 0.9962\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 4s 62us/step - loss: 0.0989 - acc: 0.9702 - top_k_categorical_accuracy: 0.9994 - top3_acc: 0.9969\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 4s 63us/step - loss: 0.0871 - acc: 0.9740 - top_k_categorical_accuracy: 0.9995 - top3_acc: 0.9974\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 4s 63us/step - loss: 0.0773 - acc: 0.9760 - top_k_categorical_accuracy: 0.9997 - top3_acc: 0.9980\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 4s 63us/step - loss: 0.0709 - acc: 0.9781 - top_k_categorical_accuracy: 0.9996 - top3_acc: 0.9978\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 4s 63us/step - loss: 0.0650 - acc: 0.9800 - top_k_categorical_accuracy: 0.9997 - top3_acc: 0.9982\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 4s 63us/step - loss: 0.0599 - acc: 0.9813 - top_k_categorical_accuracy: 0.9997 - top3_acc: 0.9987\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 4s 63us/step - loss: 0.0561 - acc: 0.9826 - top_k_categorical_accuracy: 0.9998 - top3_acc: 0.9987\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 4s 63us/step - loss: 0.0533 - acc: 0.9832 - top_k_categorical_accuracy: 0.9998 - top3_acc: 0.9987\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 4s 63us/step - loss: 0.0497 - acc: 0.9846 - top_k_categorical_accuracy: 0.9998 - top3_acc: 0.9987\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 4s 63us/step - loss: 0.0472 - acc: 0.9855 - top_k_categorical_accuracy: 0.9998 - top3_acc: 0.9991\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 4s 63us/step - loss: 0.0449 - acc: 0.9865 - top_k_categorical_accuracy: 0.9998 - top3_acc: 0.9990\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 4s 63us/step - loss: 0.0423 - acc: 0.9872 - top_k_categorical_accuracy: 0.9999 - top3_acc: 0.9990\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 4s 63us/step - loss: 0.0401 - acc: 0.9881 - top_k_categorical_accuracy: 0.9999 - top3_acc: 0.9992\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 4s 64us/step - loss: 0.0388 - acc: 0.9878 - top_k_categorical_accuracy: 0.9999 - top3_acc: 0.9993\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 4s 67us/step - loss: 0.0369 - acc: 0.9889 - top_k_categorical_accuracy: 0.9999 - top3_acc: 0.9994\n",
      "Training Time (in seconds): 76.12708274100032\n",
      "Evaluating the Model\n",
      "10000/10000 [==============================] - 0s 31us/step\n",
      "Test Accuracy: 98.57000000000001\n",
      "Test Loss: 0.4242458709906787\n",
      "Top-5 Accuracy: 100.0\n",
      "Top-3 Accuracy: 99.91\n",
      "Inference Time (in seconds): 0.30866021300016655\n",
      "CPU RAM Free: 9.2 GB\n",
      "GPU 0 ... Mem Free: 13483MB / 15079MB | Utilization  11%\n"
     ]
    }
   ],
   "source": [
    "#Model Compiling\n",
    "print(\"Compiling LeNet\")\n",
    "opt = SGD(lr=0.01)\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=opt,\n",
    "\tmetrics=[\"accuracy\", 'top_k_categorical_accuracy',top3_acc])\n",
    "\n",
    "##GPU Log file generation\n",
    "subprocess.Popen(\"nvidia-smi --query-gpu=utilization.gpu,utilization.memory --format=csv -l 1 | sed s/%//g > ./GPU-stats.log\",shell=True)\n",
    "\n",
    "#Training\n",
    "start_time = timeit.default_timer()\n",
    "print(\"Training LeNet\")\n",
    "model.fit(trainData, trainLabels, batch_size=128, epochs=20,verbose=1)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Training Time (in seconds):\",elapsed)\n",
    "\n",
    "#Inference Timing\n",
    "start_time = timeit.default_timer()\n",
    "print(\"Evaluating the Model\")\n",
    "(loss,accuracy,top_5,top_3)=model.evaluate(testData, testLabels,batch_size=128, verbose=1)\n",
    "print(\"Test Accuracy:\",accuracy*100)\n",
    "print(\"Test Loss:\",loss*10)\n",
    "print(\"Top-5 Accuracy:\",top_5*100)\n",
    "print(\"Top-3 Accuracy:\",top_3*100)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Inference Time (in seconds):\",elapsed)\n",
    "\n",
    "\n",
    "#GPU/CPU Util Function\n",
    "def mem_report():\n",
    "  print(\"CPU RAM Free: \" + humanize.naturalsize( psutil.virtual_memory().available ))\n",
    "  \n",
    "  GPUs = GPUtil.getGPUs()\n",
    "  for i, gpu in enumerate(GPUs):\n",
    "    print('GPU {:d} ... Mem Free: {:.0f}MB / {:.0f}MB | Utilization {:3.0f}%'.format(i, gpu.memoryFree, gpu.memoryTotal, gpu.memoryUtil*100))\n",
    "    \n",
    "#Generate Report\n",
    "mem_report()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WMlwDJyt4oKh"
   },
   "outputs": [],
   "source": [
    "## Generation of Activation Maps\n",
    "from keras import models\n",
    "layer_outputs = [layer.output for layer in model.layers[:4]] # Extracts the outputs of all the layers\n",
    "activation_model = models.Model(inputs=model.input, outputs=layer_outputs) # Creates a model that will return these outputs, given the model input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bU1o3DBH4_6Y"
   },
   "outputs": [],
   "source": [
    "## Generating the activation map for a input image\n",
    "img_tensor = trainData[0:1]\n",
    "activations = activation_model.predict(img_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 639
    },
    "colab_type": "code",
    "id": "iKqEJ9j2oy2O",
    "outputId": "0adf3c52-72e3-4198-fe76-d65203fb02f4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NfbTsdYHWjaFAK85omNKR15mS/abPrAGyZqwT0zkm60EpeLw2SYvqfddQU7nxevp55xXy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\nkiRJkppyIipJkiRJauov4lL3Rce0HfUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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g+5u9ySHKkmc441rdlEd1rB5Ayeb7hi50Oav5y3k+/vyczV0+39j2vu/+++Gl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\nIiIiIiJp9f8BAvj/TfAIQFIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KoiiKogwq+iCqKIqiKIqiKIqiDCofqEeUIzE89Fg8dkQX6DFjcRl3D0Vm1NH8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\n7pSBkJ0ZdX7TSaC/iCqKoiiKoiiKoiiDij6IKoqiKIqiKIq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ez5RJxRAPH1kB8bDcaojj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\n",
      "text/plain": [
       "<Figure size 1152x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AACClmIgCAAAAAFIqpT2iwcyqRadZ5V+nyds3NZ3f0n65zC+/5bjI/et5eW6k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\nbWfrKt1hq5+9WZuTv5J5y61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      "text/plain": [
       "<Figure size 1152x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Visualizing the Hidden Layer Representations\n",
    "layer_names = []\n",
    "for layer in model.layers[:4]:\n",
    "    layer_names.append(layer.name) # Names of the layers, so you can have them as part of your plot\n",
    "    \n",
    "images_per_row = 16\n",
    "for layer_name, layer_activation in zip(layer_names, activations): # Displays the feature maps\n",
    "    n_features = layer_activation.shape[-1] # Number of features in the feature map\n",
    "    size = layer_activation.shape[1] #The feature map has shape (1, size, size, n_features).\n",
    "    n_cols = n_features // images_per_row # Tiles the activation channels in this matrix\n",
    "    display_grid = np.zeros((size * n_cols, images_per_row * size))\n",
    "    for col in range(n_cols): # Tiles each filter into a big horizontal grid\n",
    "        for row in range(images_per_row):\n",
    "            channel_image = layer_activation[0,\n",
    "                                             :, :,\n",
    "                                             col * images_per_row + row]\n",
    "            channel_image -= channel_image.mean() # Post-processes the feature to make it visually palatable\n",
    "            channel_image /= channel_image.std()\n",
    "            channel_image *= 64\n",
    "            channel_image += 128\n",
    "            channel_image = np.clip(channel_image, 0, 255).astype('uint8')\n",
    "            display_grid[col * size : (col + 1) * size, # Displays the grid\n",
    "                         row * size : (row + 1) * size] = channel_image\n",
    "    scale = 1. / size\n",
    "    plt.figure(figsize=(scale * display_grid.shape[1],\n",
    "                        scale * display_grid.shape[0]))\n",
    "    plt.title(layer_name)\n",
    "    plt.grid(False)\n",
    "    plt.imshow(display_grid, aspect='auto', cmap='plasma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1147
    },
    "colab_type": "code",
    "id": "iKZ-qn3r4d2F",
    "outputId": "d6de7152-ed4d-4996-c6b0-d76b31cabdb4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 5, Actual: 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=96x96 at 0x7F17E06FFCC0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 2, Actual: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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DSRARxPP8+sUCnx2LxQKJMjAgKcq4t6DiMBKE1wTdRdd1IUfY44OOKBUU0tBv3Za0DXR+\nEjxjC44Bj9o4Ycdx6LoP1BmDJhRB7njpAQiC9BqGEQ6HKc0L92ygrp0ocBwHlkgwxQA+FO21O35P\nWBLktYB9+tZ1XZiC8NRuaX1k4AOjAJDNuGN2ByBouVw6jgPHJ5vN4p4o+Aco8IaN67oukp0wMuqO\n5OZOpxP8cCOwCcDppSsI6FsoF13XN+5K2MthK9J1OjCsqaJxIw5DEO4Vs22b8uDS6TSiYBTVIoLg\nB1H3fr9/cXHRaDT2lrlTiic4AuhbnPDjAq/1vpBfGNbBa0Oewlkl/YLLBJrNpiiKpmkmV6DN1bIs\nupqCuuP+gcFgsPdmGxw2zmYzOMDrEjQej8fj8cbnLJfLWxXpd2GHOaCSxltRp+k4TqvVgjUUi8XI\njXQcB3520HJFeGevLmAYZrFYQE6hg27dtee6Lq2ajSOkY6a7s8Mc6j5E0ojkecNHxX5PA4Kqhr1P\nfWnv2xsvXb+oKbiR7d0Nnyxa/YIXvOAFL3jBC17wghe84AUveCD+CwFWAcp+cL5YAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=96x96 at 0x7F17E4402F28>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 3, Actual: 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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SiO/7k8kE23aQBGzbns/noAxHh580nn0qjksQ4i7mMDRN67qOfOw4jqqqGKuTtU7U3Njo\nAEGwQTjUxcUF2Usku5ukIz3eXvlx94PID/g5iiIU1mmakmd3kM1pbCKALBKwVqvV/f39cDgcDoeO\n48zn8yAIHu9OisXrDQ5pmsaOR5ZlYRhiJVr4FtIeiEGIzYg+6G+DIEC3VVQv+p941YOgqH3QfJKn\nCeZBNl7zWQwLethWIIZzbM/6wAc+8IEPfOADH/jABz7wgQ+8If4HfWHuDRGEDu8AAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=96x96 at 0x7F17E06FF780>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 3, Actual: 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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INi3LkiQJjRC0GHfr2dQjw9Ywx3EMw0Blmn6X4eypBsYlr6+vu91urVbD1CrtYatWq0EQ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\nwQte8IIXvOAFL/gX4j/yo/hzsyLi8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=96x96 at 0x7F17E06FF2E8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 5, Actual: 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=96x96 at 0x7F17E06FFCC0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 0, Actual: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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IX0VR0PFDYSHsQE7HfnjHcVzXvT9l9X0fIWY6nZKtQMSVUJS/PBIX4BVaXNiCR2Iz+c+U\n/O45HLEPSlEUCEgAGz/m87lpmkRzE4LCMIQr2bZNShbyrz4wnDedCLxyD5BUrYg+UMAwKBRfZHMr\nOcU0TWwSns/n+SCCT/NZ7ODtHy/BK28kJ21D7LOlaRpTICRmVJIoHcgpm81muVyiaMru7TnFuT9m\ncPRdvLIFkSRF5ovkv1fI/2HgB3IKGqPoGT2kCeFT1A+ZCOw/0VtdN7f3be/nPYlY3Kgmhe4/Yj5H\nHHHEEUccccQRRxxxxBFHHHHEEUccccQRRxxxxBH/N/gPaJu4U2BraYoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=96x96 at 0x7F17E06FF780>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 7, Actual: 7\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGAAAABgCAIAAABt+uBvAAAL6UlEQVR4nO2ca3PayBKGR+iGBAIk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\nb3rTm3bQ/wCR4llh+qOK9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=96x96 at 0x7F17F05A9198>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 2, Actual: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=96x96 at 0x7F17F05A9198>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 2, Actual: 2\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGAAAABgCAIAAABt+uBvAAALbElEQVR4nO2cW3PiRhOGkUASkgAh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\nYqABA+AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=96x96 at 0x7F17E4402F28>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 7, Actual: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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h7fV6hmGgYfDaouHnYFuCPM8DQfYapmnCnRuGgRYNhv4REx963y+GrQhaLBZoUYmi+C1B\npmmGa/ybqAG2IgjHu1iW9TwP+oUTa2CKDKO8dXv8ILb6+0Ecx+GPI0iShJY5OVmJ4Jh0RA+93Q98\n4AMf+MAHPvCBD3zgleA/1C1FxZggHIAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=96x96 at 0x7F17F05A9198>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from google.colab.patches import cv2_imshow\n",
    "# randomly select a few testing digits\n",
    "for i in np.random.choice(np.arange(0, len(testLabels)), size=(10,)):\n",
    "\t# classify the digit\n",
    "\tprobs = model.predict(testData[np.newaxis, i])\n",
    "\tprediction = probs.argmax(axis=1)\n",
    "\n",
    "\t# extract the image from the testData if using \"channels_first\" ordering\n",
    "\tif K.image_data_format() == \"channels_first\":\n",
    "\t\timage = (testData[i][0] * 255).astype(\"uint8\")\n",
    "\n",
    "\t# otherwise we are using \"channels_last\" ordering\n",
    "\telse:\n",
    "\t\timage = (testData[i] * 255).astype(\"uint8\")\n",
    "\n",
    "\t# merge the channels into one image\n",
    "\timage = cv2.merge([image] * 3)\n",
    "\n",
    "\t# resize the image from a 28 x 28 image to a 96 x 96 image so we\n",
    "\t# can better see it\n",
    "\timage = cv2.resize(image, (96, 96), interpolation=cv2.INTER_LINEAR)\n",
    "\n",
    "\t# show the image and prediction\n",
    "\tcv2.putText(image, str(prediction[0]), (5, 20),\n",
    "\t\t\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 2)\n",
    "\tprint(\"Predicted: {}, Actual: {}\".format(prediction[0],\n",
    "\t\tnp.argmax(testLabels[i])))\n",
    "\tcv2_imshow(image)\n",
    "\tcv2.waitKey(0)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "LeNet_Mish.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
